Fingerprint-based authentication systems need to be secured against spoof attacks. In this paper, we propose completed local binary pattern (CLBP) texture descriptor with wavelet transform (WT) for fingerprint liveness detection. The fundamental basis of the proposed method is live, and spoof finger images differ in textural characteristics due to gray-level variations. These textural characteristics occur at various scales and orientations. CLBP has high discriminatory power as it takes into account local sign and magnitude difference with average gray level of an image. CLBP extended to 2-D Discrete WT (DWT), and 2-D Real Oriented Dual Tree WT (RODTWT) domain captures texture features at multiple scales and orientations. Each image was decomposed up to four levels, and CLBP features computed at each level are classified using linear and RBF kernel support vector machine (SVM) classifiers. Extensive comparisons are made to evaluate influence of wavelet decomposition level, wavelet type, number of wavelet orientations, and feature normalization ethod on fingerprint classification performance. CLBP in WT domain has proved to offer effective classification performance with simplicity of computation. While texture features at each scale contribute to performance, higher performance is achieved at lower decomposition levels of high resolution with db2 and db1 wavelets, RBF SVM and mean normalized features.